CN110556033A - Operation guiding system based on typical and accident case base of thermal power plant - Google Patents
Operation guiding system based on typical and accident case base of thermal power plant Download PDFInfo
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Abstract
The invention belongs to the field of safety control systems of power plants, and particularly relates to an operation guidance system based on typical and accident case libraries of a thermal power plant. DCS data acquisition system including thermal power plant unit, its characterized in that: the storage stores a guidance function module, stores an accident case library and typical operation; the processor encodes and grades fault information of different parts of the thermal power plant, normal threshold value ranges are divided for operation indexes of the thermal power plant through analysis of an expert industry knowledge base and historical fault data, change rules of the operation process of the indexes are mined, data sample sets in different states are constructed, and a fault triggering model is established by using a machine learning algorithm. When the trigger condition is met, the display visually displays the fault occurrence position and the data distribution rule, and displays information such as fault generation reasons and processing suggestions in the display, so that the fault early warning is accurate, and the operation is guided to be accurate and in place.
Description
Technical Field
The invention belongs to the field of safety control systems of power plants, and particularly relates to an operation guidance system based on typical and accident case libraries of a thermal power plant.
Background
the centralized control operation of the thermal power plant is an important guarantee for ensuring the safe and efficient operation of the thermal power plant, namely the operation level and the accident handling level of centralized control operators directly influence the safe and economic operation of the thermal power plant; particularly, in recent years, the number of centralized control operators is less and less (2 persons or 1 person) and the centralized control operators tend to be younger, the operation level is reduced due to unskilled business level, and when the unit is abnormally operated or has accidents, the abnormity or the accidents are enlarged due to unskilled business technology or incomplete consideration, so that the normal production of a thermal power plant is seriously influenced. Aiming at the current operation situation, a set of operation guidance system needs to be developed urgently, operation measures, cautions and specific key regulating quantity suggestions are solidified into the system, necessary guidance is provided for operation personnel during certain typical operation or accident operation, and a positive effect is exerted on promoting the safe and economic operation of the thermal power plant.
The patent CN 102289963 multivariate thermal power operation simulation operation skill training method combines DCS operation station, on-site station operation practice with DCS teaching courseware and audio and video files of on-site operation module accident treatment, but the training system only simply combines teaching data, accident treatment information and a training software system, is a fixed training operation system, and lacks operation guidance and treatment suggestions for a typical accident case base appearing on site.
Patent CN 104616124 is a real-time release method and system for safe and economic operation status of thermal power plant. A real-scene three-dimensional model of the thermal power plant is established by utilizing the SIS and MIS video monitoring systems, an expert guidance library is established in the SIS system, and operation guidance is output. However, the patent indirectly obtains production real-time data from DCS through the SIS system, the environment of the power plant is complex and changeable, and the expert knowledge base constructed in the SIS can not provide operation guidance for field operation comprehensively.
Patent CN 102930773 is a three-dimensional real-time training method and system for petrochemical devices. A three-dimensional training system of a petrochemical device is developed by utilizing a virtual reality technology, is also a set of field operation configuration system, performs complex equipment operation in a three-dimensional environment, and provides no operation suggestion guidance for possible failure of field operation.
disclosure of Invention
aiming at the defects of the technology, the patent provides the operation guiding system which has accurate early warning and guides the operation and is based on the typical and accident case libraries of the thermal power plant.
An operation guiding system based on typical and accident case libraries of a thermal power plant is characterized in that: DCS data acquisition system including thermal power plant unit, its characterized in that: also comprises a memory, a processor and a display,
the storage stores a guidance function module, stores an accident case library and typical operation;
The processor encodes and grades fault information of different parts of the thermal power plant, divides a normal threshold range for an operation index of the thermal power plant through analysis of an expert industry knowledge base and historical fault data, excavates a change rule of the index operation process, constructs data sample sets in different states, and establishes a fault trigger model by using a machine learning algorithm;
the display at least comprises a DCS intelligent employer operation display function.
when the condition of triggering the model is met, key operation steps and cautions including but not limited to key operation quantities such as valve opening and air door baffle opening are triggered and displayed on the same interface of the DCS auxiliary intelligent operation system arranged side by side on the operator operation platform and a DCS terminal screen. And establishing a typical operation or accident case operation self-learning model to realize self-learning and self-optimization, and determining key operation steps (including but not limited to key operation quantities, such as valve opening, air door baffle opening and the like) and cautions of optimization by experts.
the triggering condition of the fault triggering model is from a DCS data acquisition system of a thermal power plant unit, real-time data of a production field transmitted from the DCS data acquisition system is directly acquired, an algorithm model base is formed through historical data modeling, when the triggering condition is met, a display visually displays a fault occurrence part and a data distribution rule, and displays a fault occurrence reason and processing suggestion information in the display.
the display also shows a variation rule among adjustable parameters of process operation data of typical equipment based on typical operation, and utilizes historical data to mine the optimal working condition operation parameters so as to provide operation guidance suggestions for operators.
The accident case library and the typical operation comprise operation standards formed by the operation sequence and key cautionary matters of equipment by combining field 'operation regulations' and field personnel operation experiences.
the example of a four-tube leak in a boiler will now be described. Triggering conditions are as follows: the deviation of the water supply flow and the main steam flow is more than 80T/h, the temperature drop rate of smoke at each section of the hearth and the flue is more than 5 ℃/min (the leakage rate of the temperature drop is required to be sufficiently and generously reflected), or the deviation of two sides is more than 15 ℃, the leakage detection value of the furnace tube is more than 50 (most parameters are more than 25 year round and are recommended to be changed into 50), the negative pressure fluctuation of the hearth is more than +/-100 pa (a fixed value is determined), the current ratio of the induced draft fan is higher than that of the #3 furnace 5A and the #4 furnace 10A under the same working condition (the fixed value is larger and the difference is required due to. The key remarks are as follows: the main steam temperature and the reheat steam temperature are stabilized at about 538 ℃; the fluctuation of the water level of the steam drum is controlled to be +/-50 mm, and the steam drum is applied for shutdown in time when the flow deviation is large; paying attention to the low opening of the small motor, and increasing the output of the electric pump in time; the negative pressure of the hearth is controlled to be +/-300 Pa, and is kept +/-50 Pa after stabilization.
If the leakage amount is small (the flow deviation is less than 150T/h), the processing steps are as follows:
(1) Checking that the electric pump is reliable and standby and has starting conditions at any time;
(2) paying attention to the deviation of the water supply flow and the main steam flow, reducing the evaporation capacity in time and reducing the main steam pressure to operate;
(3) Controlling the rotation speed of the small machine to be within 5200 revolutions and the opening degree of the low regulating valve to be within 85 percent, and properly opening the high regulating valve;
(4) Unstable combustion (flame intensity fluctuation less than 80%) suggests: immediately feeding oil and stabilizing combustion;
(5) According to the load condition, the recommended evaporation capacity is maintained at 700-800 tons, and the main steam pressure is within 15 MPa;
if the leakage amount is large (the flow deviation is higher than 150T/h), the processing steps are as follows:
(1) Immediately starting the electric pump for later use;
(2) Paying attention to the deviation of the water supply flow and the main steam flow, quickly reducing the load by oil injection, and reducing the main steam pressure to operate;
(3) controlling the rotation speed of the small engine within 5200 revolutions, controlling the opening of the low regulating valve within 85 percent, properly opening the high regulating valve, and immediately increasing the output of the electric pump when the water supply flow is not maintained;
(4) Unstable combustion (flame intensity fluctuation less than 80%) suggests: immediately feeding oil and stabilizing combustion;
(5) According to the load condition, the evaporation capacity is recommended to be maintained within 700 tons, and the main steam pressure is within 14 MPa;
(6) applying for furnace shutdown.
during the use process of the accident case base and the typical operation, the operation steps in the case base are adjusted according to the actual state of the on-site unit and the accumulation of expert experience, the historical operation experience of the unit is solidified into the case base, and the historical modification information is stored.
The display includes but is not limited to a CRT display, a liquid crystal display, a VR, a visual enhancement display or a wearable human-computer interaction device.
The invention has the advantages and positive effects that:
Establishing a typical operation and accident case library, establishing a comprehensive condition judgment trigger model, and when the condition of the trigger model is met, triggering and displaying key operation steps (including key operation amount, such as valve opening, air door baffle opening and the like) and cautions on a DCS auxiliary intelligent operation system which is arranged side by side on an operator operation platform on the same interface with a DCS terminal picture; and a typical operation or accident case operation self-learning model is established to realize self-learning and self-optimization, and after the self-learning and self-optimization is confirmed by experts, the operation guidance and training of operators are realized by optimizing key operation steps (including key operation variables such as valve opening, air door baffle opening and the like) and cautions. Real-time and historical data are directly obtained through the DCS, data sources are more direct and comprehensive, operation self-optimization guidance is conducted on the basis of a typical operation library and an accident case library, and operation guidance, analysis and processing suggestions are provided for field operation and maintenance personnel.
Drawings
fig. 1 is a schematic block diagram of the present invention.
Fig. 2 is an electrical schematic of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments.
Example 1:
as shown in fig. 1 and fig. 2, the operation guidance system based on the typical and accident case base of the thermal power plant is characterized in that: DCS data acquisition system including thermal power plant unit, its characterized in that: also comprises a memory, a processor and a display,
The storage stores a guidance function module, stores an accident case library and typical operation;
The processor encodes and grades fault information of different parts of the thermal power plant, divides a normal threshold range for an operation index of the thermal power plant through analysis of an expert industry knowledge base and historical fault data, excavates a change rule of the index operation process, constructs data sample sets in different states, and establishes a fault trigger model by using a machine learning algorithm;
The display at least comprises a DCS intelligent employer operation display function. And on the basis of typical operation, researching a change rule among adjustable parameters of process operation data of typical equipment, and mining the optimal working condition operation parameters by using historical data to provide operation guidance information for operators.
when the condition of triggering the model is met, key operation steps and cautions including but not limited to key operation quantities such as valve opening and air door baffle opening are triggered and displayed on the same interface of the DCS auxiliary intelligent operation system arranged side by side on the operator operation platform and a DCS terminal screen. And establishing a typical operation or accident case operation self-learning model to realize self-learning and self-optimization, and determining key operation steps (including but not limited to key operation quantities, such as valve opening, air door baffle opening and the like) and cautions of optimization by experts.
The triggering condition of the fault triggering model is from a DCS data acquisition system of a thermal power plant unit, real-time data of a production field transmitted from the DCS data acquisition system is directly acquired, an algorithm model base is formed through historical data modeling, when the triggering condition is met, a display visually displays a fault occurrence part and a data distribution rule, and displays a fault occurrence reason and processing suggestion information in the display.
the display includes but is not limited to a CRT display, a liquid crystal display, a VR, a visual enhancement display or a wearable human-computer interaction device.
The method specifically comprises the following steps:
step (1), forming a typical operation library and an accident case library based on an operation rule and field operator experience, and determining typical operation steps, operation amount, notice items and operation names, operation steps, operation amount and notice items of accident operation;
step (2), based on DCS historical data, data division is carried out on different accidents, a machine learning algorithm is utilized to search a normal operation interval for key operation indexes of a unit, a typical and accident operation triggering model is established by referring to experience of operators, and then the model is arranged in a data processing system;
When the operation parameters exceed the settings in the step (2), triggering model parameters, extracting corresponding operation steps, operation amount and cautions from the typical operation library and the accident case library in the step (1), outputting the corresponding operation steps, operation amount and cautions to an operation guidance function module of the DCS intelligent auxiliary operation system, arranging a display screen and a DCS operation terminal side by side to provide operation guidance for operators, for example, when a steam pump tripping accident occurs, after triggering a steam pump comprehensive tripping signal, carrying out sound alarm on the system, namely 'X-X steam pump tripping' and automatically popping up an 'X-X steam pump tripping operation guidance' dialog box, displaying 'tripping initial reason', 'processing step' and 'key cautions in the processing process', relating to adjustable parameters in the processing step, displaying an optimal adjustment target value according to the current working conditions, and according to different conditions occurring in the accident processing process, the reminding content can be automatically switched to the operation which should be carried out according to the current condition. For non-accident operations such as coal mill switching, a production person can manually start a dialog box, the dialog box comprises an operation step and a key notice, and the production person can expand the coal mill switching operation according to the operation step, so that the shortage of the production person level or item skipping and item missing in the operation process can be made up.
In the actual operation process, along with the operation of the unit, the information in the typical case base and the accident case base of the power plant is optimized and modified by the power plant experts, so that the operation suggestion which is more reasonable and more in line with the actual situation of the site can be provided when the model is triggered next time.
Typical and accident case library model triggering module:
1. Coding and grading the historical data of DCS operation according to typical operation and accident operation, and accumulating characteristic data sets corresponding to different case operations and accidents;
2. evaluating and predicting the operation range of the adjustable variable for the data of typical operation and accident operation by using an algorithm;
3. Combining the 'operating regulations' and the operating experience of field personnel to form typical operation and accident case standards for the equipment operating sequence and key notice;
4. the method comprises the steps of combining a typical case base standard with algorithm adjustable parameters to form a trigger model standard, outputting corresponding fault reasons, processing steps and key cautions in a processing process on a DCS intelligent auxiliary operation platform when equipment operation meets criteria, displaying the change trend of related adjustable quantity and adjusting a target value in real time, and providing operation guidance for operators.
Typical and accident case library self-learning optimization:
(1) and forming a typical operation library and an accident case library based on the operation regulations and the experience of field operators. (2) Acquiring DCS data, carrying out modeling analysis on working condition parameters associated with typical operation and accidents, searching a parameter history optimal operation suggestion interval, triggering model early warning when the real-time values of the parameters deviate from a reasonable range by combining actual conditions on site and expert experience of a power plant, and giving early warning reasons and operation suggestion by combining a typical case library; (3) in the operation process, power plant experts optimize and modify a power plant typical case base and an accident case base so as to provide an operation suggestion which is more reasonable and more consistent with the actual situation of a site when a model is triggered next time.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (5)
1. the utility model provides an operation guidance system based on thermal power plant is typical and accident case storehouse, includes the DCS data acquisition system of thermal power plant unit which characterized in that: also comprises a memory, a processor and a display,
The storage stores a guidance function module, stores an accident case library and typical operation;
the processor encodes and grades fault information of different parts of the thermal power plant, divides a normal threshold range for an operation index of the thermal power plant through analysis of an expert industry knowledge base and historical fault data, excavates a change rule of the index operation process, constructs data sample sets in different states, and establishes a fault trigger model by using a machine learning algorithm;
the display at least comprises a DCS intelligent employer operation display function;
The triggering condition of the fault triggering model is from a DCS data acquisition system of a thermal power plant unit, real-time data of a production field transmitted from the DCS data acquisition system is directly acquired, an algorithm model base is formed through historical data modeling, when the triggering condition is met, a display visually displays a fault occurrence part and a data distribution rule, and displays a fault occurrence reason and processing suggestion information in the display.
2. the thermal power plant typical and accident case base based operation guiding system as claimed in claim 1, wherein the display further shows the variation law between adjustable parameters of the process operation data of the typical equipment based on typical operation, and the historical data is used to mine the optimal working condition operation parameters to provide operation guiding suggestions for operators.
3. the thermal power plant representative and accident case base based operation guiding system as claimed in claim 1, wherein the accident case base and the representative operation comprise operation standards formed by the operation sequence and key notice of equipment in combination of field operation regulations and field personnel operation experience.
4. The thermal power plant typical and accident case base based operation guiding system as claimed in claim 1 or 3, wherein the accident case base and typical operation adjust operation steps in the case base according to actual state of field unit and expert experience accumulation during use, and solidify historical operation experience of the unit into the case base, and save modification information of all times.
5. The thermal power plant representative and accident case base based operation guidance system according to claim 1 or 2, wherein the display includes but is not limited to a CRT display, a liquid crystal display, a VR, a visual enhancement display or a wearable human-machine interaction device.
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Cited By (6)
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| CN112632325A (en) * | 2020-12-22 | 2021-04-09 | 新疆中泰矿冶有限公司 | Remote monitoring management method and device for operation site based on digital 3D imaging |
| CN112801537A (en) * | 2021-02-22 | 2021-05-14 | 西安热工研究院有限公司 | Non-stop accident analysis method, system, medium and equipment for power generation enterprise |
| CN113268894A (en) * | 2021-07-20 | 2021-08-17 | 国能信控互联技术有限公司 | Thermal power production data management method and system based on data center station |
| CN113468710A (en) * | 2020-03-30 | 2021-10-01 | 中国石化工程建设有限公司 | Petrochemical production auxiliary operation method |
| CN113703410A (en) * | 2021-08-31 | 2021-11-26 | 华电国际电力股份有限公司深圳公司 | Wisdom operation platform of wisdom power plant |
| CN115422209A (en) * | 2022-11-07 | 2022-12-02 | 东方电气风电股份有限公司 | Wind power case data processing system and method |
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| CN115422209A (en) * | 2022-11-07 | 2022-12-02 | 东方电气风电股份有限公司 | Wind power case data processing system and method |
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